Anaerobic membrane reactor: Biomethane from chicken manure and high-quality effluent

Chicken manure was treated in a pilot scale reactor anaerobic membrane bioreactor constituted by a completely mixed reactor combined with an ultrafiltration tube-shaped membrane in a side-stream configuration. The process operated under mesophilic condition and the inhibition of high concentration of ammonia was avoided using an ammonia stripping system. The experimental plan included a preliminary evaluation, where organic loading rates between 1.0 and 7.6 kgVS/m3/day were tested. The organic load higher than 4 kgVS/m3/d caused the accumulation of volatile fatty acids and process instability. Application of the ammonia stripping was also evaluated. The best performances were achieved using a retention time of 21 days, an organic load between 1.4 and 2.0 kgVS/m3/d, and the recirculation of stripped permeate. Reduction of the ammonia permeate content by 90% through stripping and utilization of a mixture of chicken manure/water/permeate in a ratio of 0.22/0.72/0.72 w/w led to a specific biogas production of 0.59 m3biogas/kgVS and methane content of 66–69%. The ammonia thus removed can be recovered by sulphuric acid treatment as ammonium sulphate, which can be used as a fertilizer. The proposed configuration allowed satisfactory biogas production with appropriate methane percentages, recovery of ammonium sulphate, and a high-quality effluent

Generalization Properties of Machine Learning-based Raman Models

We investigate the generalization capabilities of neural network-based Raman amplifier models. The new proposed model architecture, including fiber parameters as inputs, can predict Raman gains of fiber types unseen during training, unlike previous fiber-specific models

Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning

A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated. We employ a single-layer neural network to learn the mapping from the gain profiles to the pump powers and noise figures. The obtained results show highly accurate gain profile designs and noise figure predictions, with a maximum error on average of ∼ 0.3 dB. This framework provides a comprehensive characterization of the Raman amplifier and thus is a valuable tool for predicting the performance of next-generation optical communication systems, expected to employ Raman amplification

Experimental Characterization of Raman Amplifier Optimization through Inverse System Design

Optical communication systems are always evolving to support the need for ever-increasing transmission rates. This demand is supported by the growth in complexity of communication systems which are moving towards ultra-wideband transmission and space-division multiplexing. Both directions will challenge the design, modeling, and optimization of devices, subsystems, and full systems. Amplification is a key functionality to support this growth and in this context, we recently demonstrated a versatile machine learning framework for designing and modeling Raman amplifiers with arbitrary gains. In this article, we perform a thorough experimental characterization of such machine learning framework. The applicability of the proposed approach, as well as its ability to accurately provide flat and tilted gain-profiles, are tested on several practical fiber types, showing errors below 0.5 dB. Moreover, as channel power optimization is heavily employed to further enhance the transmission rate, the tolerance of the framework to variations in the input signal spectral profile is investigated. Results show that the inverse design can provide highly accurate gain-profile adjustments for different input signal power profiles even not considering this information during the training phase

Optimization of Raman amplifiers using machine learning

It has been recently demonstrated that neural networks can learn the complex pump–signal relations in Raman amplifiers. Here we experimentally show how these neural network models are applied to provide highly–accurate Raman amplifier designs and flexible configuration for ultra–wideband optical communication systems

Fiber-Agnostic Machine Learning-Based Raman Amplifier Models

In this paper, we show that by combining experimental data from different optical fibers, we can build a fiber-agnostic neural-network to model the Raman amplifier. The fiber-agnostic NN model can predict the gain profile of a new fiber type (unseen by the model during training) with a maximum absolute error as low as 0.22 dB. We show that this generalization is only possible when the unseen fiber parameters are similar to the fibers used to build the model. Therefore, a training dataset with a wide range of optical fibers parameters is needed to enhance the chance of accurately predicting the gain of a new fiber. This implies that time-consuming experimental measurements of various fiber types can be avoided. For that, here we extend and improve our general model by numerically generating the dataset. By doing so, it is possible to generate uniformly distributed data that covers a wide range of optical fiber types. The results show that the averaged maximum prediction error is reduced when compared to the limited experimental data-based general models. As the second and final contribution of this work, we propose the use of transfer learning (TL) to re-train the numerical data-based general model using just a few experimental measurements. Compared with the fiber-specific models, this TL-upgraded general model reaches very similar accuracy, with just 3.6% of the experimental data . These results demonstrate that the already fast and accurate NN-based RA models can be upgraded to have strong fiber generalization capabilities

Machine learning applied to inverse systems design

In this work, we will give an overview of some of the most recent and successful applications of machine learning based inverse system designs in photonic systems. Then, we will focus on our recent research on the Raman amplifier inverse design. We will show how the machine learning framework is optimized to generate on-demand arbitrary Raman gain profiles in a controlled and fast way and how it can become a key feature for future optical communication systems

Perspectives of Long-Haul WDM Transmission Systems Based on Phase-Insensitive Fiber-Optic Parametric Amplifiers

International audienceThe deployment of phase-insensitive fiber-optic parametric amplifiers (PI-FOPAs) as inline amplifiers in long-haul WDM transmission systems is discussed, and it is outlined how to design PI-FOPAs to be a valuable upgrade option for this application

Inverse System Design Using Machine Learning: The Raman Amplifier Case

A wide range of highly-relevant problems in programmable and integrated photonics, optical amplification, and communication deal with inverse system design. Typically, a desired output (usually a gain profile, a noise profile, a transfer function or a similar continuous function) is given and the goal is to determine the corresponding set of input parameters (usually a set of input voltages, currents, powers, and wavelengths). We present a novel method for inverse system design using machine learning and apply it to Raman amplifier design. Inverse system design for Raman amplifiers consists of selecting pump powers and wavelengths that would result in a targeted gain profile. This is a challenging task due to highly-complex interaction between pumps and Raman gain. Using the proposed framework, highly-accurate predictions of the pumping setup for arbitrary Raman gain profiles are demonstrated numerically in C and C+L-band, as well as experimentally in C band, for the first time. A low mean (0.46 and 0.35 dB) and standard deviation (0.20 and 0.17 dB) of the maximum error are obtained for numerical (C+L-band) and experimental (C-band) results, respectively, when employing 4 pumps and 100 km span length. The presented framework is general and can be applied to other inverse problems in optical communication and photonics in general